Asthma exacerbations contribute to considerable disease morbidity and account for nearly half of all asthma- related costs. Moreover, certain population groups, such as African American and Latino individuals, suffer disproportionately from these complications with rates of asthma-related emergency department visits, hospitalizations, and deaths nearly 3-5 times higher than those of European Americans. There are multiple reasons to believe that individuals who suffer severe exacerbations are genetically predisposed: 1) prior events are among the strongest predictors of future exacerbations, 2) genetic ancestry has been shown to be an independent predictor of exacerbations, and 3) calculations of exacerbation heritability suggest that 30-55% of this trait?s variance may be attributed to additive genetic effects. Nevertheless, we do not currently have genetic biomarkers that can be used clinically to reliably predict susceptibility to asthma exacerbations. Such measures could transform asthma care if they resulted in the early recognition and appropriate treatment of individuals at risk. In this application, we will utilize the enormous amount of whole genome sequence (WGS) data that is being generated on our Asthma Translational Genomics Collaborative (ATGC) as part of the NHLBI?s Trans-Omic Precision Medicine (TOPMed) Program. The ATGC comprises 8 cohort studies and 10,819 patients with asthma (7,530 African Americans and 3,081 Latinos). The Genetic Epidemiology of Asthma in Costa Rica (CRA) cohort with its WGS data will also participate (n=1,765). In Aim 1, we will focus on the genomics of asthma exacerbations through the following sub-aims: a) refine our estimates of exacerbation heritability using a WGS data; b) use admixture mapping to identify chromosomal regions likely to harbor risk variants for exacerbations; c) fine-map the aforementioned regions for risk variants using available WGS data; d) replicate these associations in other ATGC cohorts and in the CRA cohort; and e) assess variants for their association with future exacerbations using available prospective clinical data. In Aim 2, we will focus on the transcriptomics of asthma exacerbations. Namely, we will use RNA-sequence data derived from the whole blood transcriptome to identify genes whose expression associated with severe exacerbations (Aim 2a), and we will identify genes whose expression is associated with the genotype of variants identified in Aim 1 (Aim 2b). Aim 3 will focus on the proteomics of asthma exacerbations. Banked serum will be used to assess the proteome of individuals from phenotype extremes (i.e., serum collected from individuals prior to a severe exacerbation vs. serum from individuals with asthma who don?t experience exacerbations). Using mass spectrometry, we will broadly assess serum for proteins differentially expressed between these groups (i.e., an untargeted proteomic approach), and we will use the information gleaned from the genomic, transcriptomic, and untargeted proteomic analyses to assess specific proteins (i.e., a targeted proteomic approach) for expression differences in additional groups of individuals at phenotype extremes.